DCNN Developed for Detection and Assessing the Perfusion of PTG

ConditionThyroidectomy
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-70
SponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen University

About this trial

Since the anatomical location and appearance of the parathyroid gland (PTG) vary, detection of the PTG and preserving the blood supply are among the difficulties encountered during a thyroidectomy procedure. We are planning to train a deep convolutional neural network based on a larger sample of endoscopic images to develop a model to assist surgeons in detection of PTG during endoscopic thyroidectomy. Furthermore, we would like to train a DCNN to predict blood perfusion based on endoscopic images comparing to indocyanine green fluorescence angiography as reference standard, and assess the performance of DCNN in predicting postoperative hypoparathyroidism.

Eligibility criteria

Qualifiers

The patients who undergo endoscopic thyroidectomy

Disqualifiers

hyperparathyroidism

hypoparathyroidism

neck surgery history

cervical radiotherapy history

Trial design

Treatments tested in this trial

  • a deep convolutional neural network

Treatment groups

No treatment groups listed